AI Policy Development in U.S. Remains in Formative Phases
U.S. Policymakers Neglect Inference Optimization in AI Strategies
The United States has prioritized access to artificial intelligence (AI) compute, reshaping policies in numerous sectors, such as energy production, export controls, and infrastructure planning. However, the focus has predominantly been on training, the phase where AI models acquire knowledge, rather than inference, the stage where they apply that knowledge to user requests.
AI training comprises pre-training and fine-tuning, where models learn basic and specific abilities, respectively. Inference, in contrast, is the application of the acquired knowledge to generate responses. For instance, chatbots use inference to answer user queries or prompts. The advancements in AI are largely occurring during the inference phase, but U.S. policies are not well-aligned with this progress, favoring training over inference.
Recent developments in AI have highlighted the importance of optimizing inference for improved performance, scalability, and cost efficiency. OpenAI's o1 model, released in December 2024, showcased the benefits of optimizing inference, as it boosted the model's accuracy while significantly increasing response time. DeepSeek's R1 model, released in January 2025, demonstrated the potential for efficiency gains through quantization, which reduces the numerical precision of calculations during inference, thereby lowering memory and compute requirements with minimal loss in accuracy.
The oversight of U.S. policymakers in addressing inference optimization affects deployment strategies, export controls, and opportunities to boost competitiveness. For instance, the deployment of U.S. AI models abroad requires local infrastructure support for inference, as it reduces latency, cuts costs, and ensures reliable service. Moreover, the Chinese government is investing heavily in digital infrastructure abroad, potentially jeopardizing the global deployment of U.S. models. Additionally, export controls focusing on training-optimized chips may miss the mark, as inference has become the engine driving applied AI.
The United States must reevaluate its AI policy to better align with the frontier of AI progress. Promoting best practices and standards, supporting research and development, fostering flexible and scalable deployment infrastructure, addressing cost and resource efficiency, and prioritizing enterprise-grade support and security are key areas of focus to optimize AI inference for improved performance, scalability, and cost efficiency. This shift could unlock smarter decisions across industries and support the global competitiveness of U.S. AI models.
- The focus of U.S. policies in AI strategies is predominantly on the training phase, neglecting inference optimization, a crucial stage for applying acquired knowledge to user requests.
- The advancements in AI are primarily occurring during the inference phase, as demonstrated by models like OpenAI's o1 and DeepSeek's R1, showcasing improved performance, scalability, and cost efficiency through optimized inference.
- U.S. policymakers' oversight of inference optimization affects deployment strategies, as inference local infrastructure support reduces latency, lowers costs, and ensures reliable service abroad.
- To remain competitive and optimize AI inference performance, scalability, and cost efficiency, the United States must reevaluate its AI policy, focusing on best practices, standards, research and development, flexible deployment infrastructure, cost efficiency, enterprise-grade support, and security.
